Serveur Model Context Protocol (MCP)

Claude-context is a retrieval-augmented generation pipeline and semantic code search tool. It functions as an LLM codebase indexer and RAG context provider, designed to index local directories and retrieve relevant code files to provide context for large language models. The system operates as a hybrid search engine that combines keyword matching with dense vector search. This allows for the retrieval of code snippets and logic using natural language queries based on meaning rather than exact text matches. The project covers codebase indexing and search index management, utilizing asynchrono

git-mcp is a Model Context Protocol server that transforms Git repositories and static sites into structured context providers for AI assistants. It functions as a documentation retrieval tool and repository indexer, exposing codebases and project files as standardized tools to reduce hallucinations in large language model responses. The project converts raw repository files, READMEs, and external URLs into formats optimized for token consumption. It enables AI agents to perform query-based code searches and retrieve specific sections of project documentation to maintain up-to-date technical

Repomix is an AI-focused development utility designed to prepare local and remote codebases for analysis, review, and automated interaction. It functions as a codebase context bundler and a Model Context Protocol server, aggregating project files into structured documents that are optimized for ingestion by large language models. By serving as a bridge between local repositories and external intelligence agents, the tool facilitates real-time codebase inspection and automated development workflows. The system distinguishes itself through rigorous repository token management and security-consc

Codegraph is a local codebase indexer and static analysis graph database that serves as a context provider for AI agents. It parses multiple programming languages into a searchable knowledge graph of symbols and dependencies, exposing these relationships to AI tools through the Model Context Protocol. The project distinguishes itself by aggregating relevant code snippets and symbol flows to reduce token usage for large language models. It automates the configuration of server settings and steering instructions across various AI agent platforms and command line editors to enable automatic code

LEANN is a framework for local retrieval augmented generation and vector indexing. It functions as a system for building local knowledge bases and source code search engines that combine large language models with retrieved private data to generate context-aware responses. The project distinguishes itself through a vision-model based document layout extractor for parsing complex PDF figures and diagrams, and a source code search engine that employs structure-aware chunking to preserve function and class boundaries. It also implements the Model Context Protocol to integrate real-time data sour